{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## 准备数据集"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import torch\n",
    "from torchvision import datasets,transforms\n",
    "import torchvision\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T09:00:22.215644400Z",
     "start_time": "2023-10-31T08:59:53.838867300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "data_path=\"F:/图像识别数据集/vechs\"\n",
    "import os\n",
    "data_transforms = {\n",
    "    'train':transforms.Compose([\n",
    "        transforms.Resize(230),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
    "    ]),\n",
    "    'test':transforms.Compose([\n",
    "        transforms.Resize(230),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
    "    ])\n",
    "}\n",
    "\n",
    "trainset = datasets.ImageFolder(os.path.join(data_path,'train'),transform=data_transforms['train'])\n",
    "testset = datasets.ImageFolder(os.path.join(data_path,'test'),transform=data_transforms['test'])\n",
    "train_loader = torch.utils.data.DataLoader(trainset,batch_size=5,num_workers=4,shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(testset,batch_size=5,num_workers=4,shuffle=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T09:22:19.794856500Z",
     "start_time": "2023-10-31T09:22:19.763623600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "['大车', '小车', '小车', '小车', '小车']"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def imshow(inputs):\n",
    "    inputs = inputs/2+0.5\n",
    "    inputs = inputs.numpy().transpose(1,2,0)\n",
    "    plt.imshow(inputs)\n",
    "    plt.show()\n",
    "\n",
    "inputs,labels = next(iter(test_loader))\n",
    "class_names = ['大车','小车']\n",
    "classes = []\n",
    "imshow(torchvision.utils.make_grid(inputs))\n",
    "for i in labels:\n",
    "    classes.append(class_names[i])\n",
    "classes"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T09:22:26.505601Z",
     "start_time": "2023-10-31T09:22:20.345603800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "## 训练函数\n",
    "def train_loop(model,loss_fn,optimizer,n_epochs,train_loader):\n",
    "    for epoch in range(n_epochs):\n",
    "        loss_train = 0.0\n",
    "        for i,data in enumerate(train_loader,0):\n",
    "            imgs,labels = data\n",
    "            imgs,labels = imgs.cuda(),labels.cuda()\n",
    "            outputs = model(imgs)\n",
    "            loss = loss_fn(outputs,labels)\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            loss_train+=loss.item()\n",
    "            if i%10==9:\n",
    "                print(\"Epoch:{}, i:{}, 训练损失:{}\".format(epoch+1,i+1,loss_train/len(train_loader)))\n",
    "                loss_train=0.0"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T09:29:18.293821600Z",
     "start_time": "2023-10-31T09:29:18.282637Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "# 测试函数\n",
    "def test_loop(model,test_loader):\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for data in test_loader:\n",
    "            imgs,labels = data\n",
    "            imgs,labels = imgs.cuda(),labels.cuda()\n",
    "            outputs = model(imgs)\n",
    "            _,preds = torch.max(outputs,dim=1)\n",
    "            total+=labels.shape[0]\n",
    "            correct+=int((labels==preds).sum())\n",
    "    print('测试集精度:{:.3f}%'.format(correct/total*100))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T09:33:56.131707Z",
     "start_time": "2023-10-31T09:33:56.117446100Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 上resnet"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet101_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet101_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet(\n",
      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu): ReLU(inplace=True)\n",
      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "  (layer1): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer2): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer3): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (4): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (5): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (6): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (7): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (8): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (9): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (10): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (11): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (12): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (13): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (14): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (15): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (16): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (17): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (18): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (19): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (20): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (21): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (22): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer4): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "resnet = torchvision.models.resnet101(pretrained=True)\n",
    "print(resnet)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T09:17:37.203955200Z",
     "start_time": "2023-10-31T09:17:35.008804600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "for p in resnet.parameters():\n",
    "    p.requires_grad=False\n",
    "\n",
    "resnet.fc = nn.Linear(2048,2,bias=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T09:24:33.431082100Z",
     "start_time": "2023-10-31T09:24:33.407054700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:1, i:10, 训练损失:0.028855883684314666\n",
      "Epoch:1, i:20, 训练损失:0.022828581574998917\n",
      "Epoch:1, i:30, 训练损失:0.0329352331149285\n",
      "Epoch:1, i:40, 训练损失:0.010238275359399984\n",
      "Epoch:1, i:50, 训练损失:0.04161700278093092\n",
      "Epoch:1, i:60, 训练损失:0.03547537931409039\n",
      "Epoch:2, i:10, 训练损失:0.04208262029607765\n",
      "Epoch:2, i:20, 训练损失:0.05653835515506932\n",
      "Epoch:2, i:30, 训练损失:0.05231683701276779\n",
      "Epoch:2, i:40, 训练损失:0.03363275715363685\n",
      "Epoch:2, i:50, 训练损失:0.01634794291200452\n",
      "Epoch:2, i:60, 训练损失:0.04382196966497624\n"
     ]
    }
   ],
   "source": [
    "import torch.optim as optim\n",
    "resnetcuda = resnet.cuda()\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(resnetcuda.parameters(),lr=0.001,momentum=0.9)\n",
    "train_loop(model=resnetcuda,loss_fn=loss_fn,optimizer=optimizer,train_loader=train_loader,n_epochs=2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T09:29:46.537457100Z",
     "start_time": "2023-10-31T09:29:22.676884Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集精度:100.000%\n"
     ]
    }
   ],
   "source": [
    "test_loop(resnetcuda.eval(),test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T09:34:20.780622100Z",
     "start_time": "2023-10-31T09:34:14.264592500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "真实值--预测值\n",
      "大车 -- 大车\n",
      "小车 -- 小车\n",
      "小车 -- 小车\n",
      "小车 -- 小车\n",
      "小车 -- 小车\n"
     ]
    }
   ],
   "source": [
    "resnet_pred = resnetcuda.eval()\n",
    "outputs=resnet_pred(inputs.cuda())\n",
    "_,preds = torch.max(outputs,dim=1)\n",
    "print('真实值--预测值')\n",
    "for i,j in zip(labels,preds):\n",
    "    print(class_names[i],'--',class_names[j])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T09:37:50.245955400Z",
     "start_time": "2023-10-31T09:37:50.015956400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## vgg网络"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace=True)\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (6): ReLU(inplace=True)\n",
      "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (8): ReLU(inplace=True)\n",
      "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (13): ReLU(inplace=True)\n",
      "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (15): ReLU(inplace=True)\n",
      "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (18): ReLU(inplace=True)\n",
      "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (20): ReLU(inplace=True)\n",
      "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (22): ReLU(inplace=True)\n",
      "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (25): ReLU(inplace=True)\n",
      "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (27): ReLU(inplace=True)\n",
      "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (29): ReLU(inplace=True)\n",
      "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Dropout(p=0.5, inplace=False)\n",
      "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): Dropout(p=0.5, inplace=False)\n",
      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "vgg = torchvision.models.vgg16(pretrained=True)\n",
    "print(vgg)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T09:44:51.938982600Z",
     "start_time": "2023-10-31T09:44:47.768739900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "for p in vgg.parameters():\n",
    "    p.requires_grad=False\n",
    "\n",
    "vgg.classifier = nn.Sequential(\n",
    "    nn.Linear(25088,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(4096,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(4096,2,bias=True)\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T09:48:37.044987900Z",
     "start_time": "2023-10-31T09:48:35.981985700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:1, i:10, 训练损失:0.09621725903182733\n",
      "Epoch:1, i:20, 训练损失:0.037782124686436575\n",
      "Epoch:1, i:30, 训练损失:0.017462784332818674\n",
      "Epoch:1, i:40, 训练损失:0.004371485962975221\n",
      "Epoch:1, i:50, 训练损失:0.006283862234787924\n",
      "Epoch:1, i:60, 训练损失:0.004799546047923018\n",
      "Epoch:2, i:10, 训练损失:0.004696616846648213\n",
      "Epoch:2, i:20, 训练损失:0.01511956910511143\n",
      "Epoch:2, i:30, 训练损失:0.009983242665928956\n",
      "Epoch:2, i:40, 训练损失:0.0008112971210989673\n",
      "Epoch:2, i:50, 训练损失:0.002796884931978907\n",
      "Epoch:2, i:60, 训练损失:0.00017496261442248083\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(vgg.parameters(),lr=0.001,momentum=0.9)\n",
    "train_loop(model=vgg.cuda(),loss_fn=loss_fn,optimizer=optimizer,n_epochs=2,train_loader=train_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T09:51:02.863479600Z",
     "start_time": "2023-10-31T09:50:27.489746200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集精度:100.000%\n"
     ]
    }
   ],
   "source": [
    "test_loop(model=vgg.cuda().eval(),test_loader=test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T09:51:59.808006600Z",
     "start_time": "2023-10-31T09:51:52.479385Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## alexnet网络"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AlexNet(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (7): ReLU(inplace=True)\n",
      "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (9): ReLU(inplace=True)\n",
      "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.5, inplace=False)\n",
      "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): Dropout(p=0.5, inplace=False)\n",
      "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (5): ReLU(inplace=True)\n",
      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "alexnet = torchvision.models.alexnet(pretrained=True)\n",
    "print(alexnet)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T10:42:30.535560300Z",
     "start_time": "2023-10-31T10:42:28.939635500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [],
   "source": [
    "for p in alexnet.parameters():\n",
    "    p.requires_grad=False\n",
    "alexnet.classifier = nn.Sequential(\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(9216,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(4096,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Linear(4096,2)\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T10:57:07.929068900Z",
     "start_time": "2023-10-31T10:57:07.577715400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:1, i:10, 训练损失:0.1021453796840105\n",
      "Epoch:1, i:20, 训练损失:0.09874168038368225\n",
      "Epoch:1, i:30, 训练损失:0.042008898877462404\n",
      "Epoch:1, i:40, 训练损失:0.019658010697267095\n",
      "Epoch:1, i:50, 训练损失:0.023090374747627092\n",
      "Epoch:1, i:60, 训练损失:0.02949321534118203\n",
      "Epoch:2, i:10, 训练损失:0.009970039186510639\n",
      "Epoch:2, i:20, 训练损失:0.017423887022381616\n",
      "Epoch:2, i:30, 训练损失:0.013338651189382081\n",
      "Epoch:2, i:40, 训练损失:0.01466450916238694\n",
      "Epoch:2, i:50, 训练损失:0.0033271059078511735\n",
      "Epoch:2, i:60, 训练损失:0.006399863879539867\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(alexnet.parameters(),lr=0.001,momentum=0.9)\n",
    "train_loop(model=alexnet.cuda(),loss_fn=loss_fn,optimizer=optimizer,n_epochs=2,train_loader=train_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T11:00:58.065576700Z",
     "start_time": "2023-10-31T11:00:00.479733100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集精度:95.455%\n"
     ]
    }
   ],
   "source": [
    "test_loop(alexnet.cuda().eval(),test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T11:01:49.639019300Z",
     "start_time": "2023-10-31T11:01:42.930399400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## squeezeNet"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=SqueezeNet1_1_Weights.IMAGENET1K_V1`. You can also use `weights=SqueezeNet1_1_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SqueezeNet(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (3): Fire(\n",
      "      (squeeze): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (4): Fire(\n",
      "      (squeeze): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (6): Fire(\n",
      "      (squeeze): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (7): Fire(\n",
      "      (squeeze): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (9): Fire(\n",
      "      (squeeze): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (10): Fire(\n",
      "      (squeeze): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (11): Fire(\n",
      "      (squeeze): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (12): Fire(\n",
      "      (squeeze): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.5, inplace=False)\n",
      "    (1): Conv2d(512, 1000, kernel_size=(1, 1), stride=(1, 1))\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "squeezenet = torchvision.models.squeezenet1_1(pretrained=True)\n",
    "print(squeezenet)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T11:03:03.035484900Z",
     "start_time": "2023-10-31T11:03:02.470084Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [],
   "source": [
    "for p in squeezenet.parameters():\n",
    "    p.requires_grad=False\n",
    "\n",
    "squeezenet.classifier = nn.Sequential(\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Conv2d(512,2,kernel_size=(1,1),stride=(1,1)),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.AdaptiveAvgPool2d(output_size=(1,1))\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T11:06:11.619195200Z",
     "start_time": "2023-10-31T11:06:11.579200500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:1, i:10, 训练损失:0.0021994071435297792\n",
      "Epoch:1, i:20, 训练损失:0.0027802931669656736\n",
      "Epoch:1, i:30, 训练损失:0.0025923116780057947\n",
      "Epoch:1, i:40, 训练损失:0.0034062914802219534\n",
      "Epoch:1, i:50, 训练损失:0.0105125444691216\n",
      "Epoch:1, i:60, 训练损失:0.008803653271704698\n",
      "Epoch:2, i:10, 训练损失:0.0015755817029323642\n",
      "Epoch:2, i:20, 训练损失:0.00605269284636454\n",
      "Epoch:2, i:30, 训练损失:0.007712904529905588\n",
      "Epoch:2, i:40, 训练损失:0.0031865783550201142\n",
      "Epoch:2, i:50, 训练损失:0.002287064643660713\n",
      "Epoch:2, i:60, 训练损失:0.0031623971671415644\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(squeezenet.parameters(),momentum=0.9,lr=0.001)\n",
    "train_loop(model=squeezenet.cuda(),loss_fn=loss_fn,optimizer=optimizer,n_epochs=2,train_loader=train_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T11:09:06.570757100Z",
     "start_time": "2023-10-31T11:08:49.641759200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集精度:97.727%\n"
     ]
    }
   ],
   "source": [
    "test_loop(squeezenet.cuda().eval(),test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T11:09:13.920515500Z",
     "start_time": "2023-10-31T11:09:08.596517500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [],
   "source": [
    "## 持久化squeeze模型\n",
    "torch.save(squeezenet.cuda().eval(),'squeeze_cuda_eval')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T11:18:48.826235300Z",
     "start_time": "2023-10-31T11:18:47.539595300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SqueezeNet(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (3): Fire(\n",
      "      (squeeze): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (4): Fire(\n",
      "      (squeeze): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (6): Fire(\n",
      "      (squeeze): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (7): Fire(\n",
      "      (squeeze): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (9): Fire(\n",
      "      (squeeze): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (10): Fire(\n",
      "      (squeeze): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (11): Fire(\n",
      "      (squeeze): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (12): Fire(\n",
      "      (squeeze): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.5, inplace=False)\n",
      "    (1): Conv2d(512, 2, kernel_size=(1, 1), stride=(1, 1))\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "## 导入模型\n",
    "sq = torch.load('squeeze_cuda_eval')\n",
    "print(sq)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T11:20:12.927008400Z",
     "start_time": "2023-10-31T11:20:12.656011600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "真实值--预测值\n",
      "大车 --- 大车\n",
      "小车 --- 小车\n",
      "小车 --- 小车\n",
      "小车 --- 小车\n",
      "小车 --- 小车\n"
     ]
    }
   ],
   "source": [
    "outputs = sq(inputs.cuda())\n",
    "_,preds = torch.max(outputs,dim=1)\n",
    "print('真实值--预测值')\n",
    "for i,j in zip(labels,preds):\n",
    "    print(class_names[i],'---',class_names[j])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T11:22:22.520923200Z",
     "start_time": "2023-10-31T11:22:22.427927300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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